With the advancement in the field of design of adaptive filter it is expected that the convergence will improve correctness in the estimates of output. Adaptive filter system focus on integrity in the existing lms alg...
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ISBN:
(纸本)9781509006656
With the advancement in the field of design of adaptive filter it is expected that the convergence will improve correctness in the estimates of output. Adaptive filter system focus on integrity in the existing lms algorithm including FIR filter, with the computation using different data formats. Adaptive filter system support various applications with the objective to provide stable system performance. Current adaptive filter system needs in depth investigation in the computing domain to enhance the quality of the estimates. Hence interval arithmetic domain is used to increase the precision of computation. Proposed solution to this research task in this aspect is examined for a sine signal.
This paper proposes the least mean square (lms) algorithm based versatile, vector, and fault tolerant adaptive finite impulse response (FIR) filter designs. Here, the M-taps versatile design is to perform the filter o...
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ISBN:
(纸本)9789813297661;9789813297678
This paper proposes the least mean square (lms) algorithm based versatile, vector, and fault tolerant adaptive finite impulse response (FIR) filter designs. Here, the M-taps versatile design is to perform the filter operation with the number of filter co-efficients varied from 2 to M. The M-taps vector design is to perform left perpendicular left perpendicular M/L right perpendicular numbers of L-taps filter operations in parallel, where M >= L. The fault tolerantM-taps filter is to perform the (M - N)-taps fault free filter operation under the N numbers of faulty filter kernels, where (M - N) >= 2. All the existing and proposed designs are implemented with 45 nm CMOS technology. The proposed 16-taps vector adaptive filter design achieves 93% of improvement in throughput as compared with the distributed arithmetic based design.
lms algorithm based on the S-function has a small amount of calculation, faster convergence rate and good tracking properties for time-varying systems. But when the signal's error is small, the step factor changes...
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ISBN:
(纸本)9781510600294
lms algorithm based on the S-function has a small amount of calculation, faster convergence rate and good tracking properties for time-varying systems. But when the signal's error is small, the step factor changes too fast, system identification is not quick enough and the controllable variables are few. To solve the shortcomings, an improved S-function algorithm has been proposed. Simulation results show that the convergence rate of the algorithm is superior to other improved algorithms, and the tracking property for the time-varying system is better than the improved normalized lms algorithms. The algorithm proposed in this paper not only overcomes the discrepancy between the signal's error and step factor, but also makes the algorithm more flexible by introducing a new controllable variable.
A variable step-size is necessary in the least-mean-square (lms) algorithm to achieve both fast convergence and a small final excess mean-square estimation error. As a well-studied area, many variations of the lms alg...
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ISBN:
(纸本)9781424440665
A variable step-size is necessary in the least-mean-square (lms) algorithm to achieve both fast convergence and a small final excess mean-square estimation error. As a well-studied area, many variations of the lms algorithm with variable step-sizes have been proposed in the literature. A common point in these algorithms is that the step-size is computed according to some pre-specified formulas with preset control parameters, and therefore the generated step-size is a predetermined constant at each single time point. In this paper, we propose a novel parameter-free step-size adjustment approach, in which the step-size is viewed as a variable, and rechosen at each new time point to minimize a least-squares cost function. Experiments for the linear prediction of random processes show the effectiveness of the proposed approach in rapidly driving the mean-square estimation error to a small final steady-state value. The most significant feature of the new approach is that no control parameters need to be set in advance.
In this paper, we propose an l(0)-norm penalized shrinkage linear least mean squares (l(0)-SH-lms) algorithm for an adaptive decision feedback equalizer (DFE). The proposed algorithm utilizes the priori and the poster...
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ISBN:
(纸本)9781467399784
In this paper, we propose an l(0)-norm penalized shrinkage linear least mean squares (l(0)-SH-lms) algorithm for an adaptive decision feedback equalizer (DFE). The proposed algorithm utilizes the priori and the posteriori errors to calculate the varying step-size. Thus a larger coefficient produces a larger increment to accelerate the convergence, and a small coefficient gives a smaller increment to improve the estimation accuracy, so the algorithm can adapt to the time-varying channel efficiently. Meanwhile, a l(0)-norm penalty term is introduced in the cost function to improve the applicability to a sparse system. Simulation results show that, compared with the conventional lms-type algorithms, the proposed algorithm achieves better performance in both the convergence rate and steady-state misalignment for the sparse channels. When the proposed algorithm is applied to the DFE, the equalization performance is clearly improved.
The adaptive noise cancellation system by lms algorithm need not to know the prior knowledge of input speech signal and noise, and can carry out denoise. In this paper, we present a general approach to using Simulink ...
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ISBN:
(纸本)9783037851036
The adaptive noise cancellation system by lms algorithm need not to know the prior knowledge of input speech signal and noise, and can carry out denoise. In this paper, we present a general approach to using Simulink to build adaptive filter which may denoise for noise added speech signal. Simulation results show that this method has the good suppression ability for the noise of collection speech signal.
Using the Least Mean Square (lms) algorithm, this paper simulates two smart antenna models under the same signal environment. By analyzing and comparing 8-antenna elements linear array and 2*4- antenna elements rectan...
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ISBN:
(纸本)9783037850732
Using the Least Mean Square (lms) algorithm, this paper simulates two smart antenna models under the same signal environment. By analyzing and comparing 8-antenna elements linear array and 2*4- antenna elements rectangular array, this paper proposes the way to design the heterotypic antenna model. This antenna array can help system to gain similar performance of signal to interference plus noise radio(SINR) with reduced size of the antenna. The paper realizes the simulation of two models with the lms algorithm. The antenna pattern and the curve about convergence of error have been given. It also analyzes the influence of the step length factor on lms algorithm performance, and finds out the reasonable step to ensure fast algorithm convergence and to suppress interference effectively.
In this study, the lms algorithm was employed to detect and track baseline wanders introduced during the acquisition of electrocardiogram signals. The baseline wander was removed from the signal and ECG is corrected. ...
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ISBN:
(纸本)9781728124209
In this study, the lms algorithm was employed to detect and track baseline wanders introduced during the acquisition of electrocardiogram signals. The baseline wander was removed from the signal and ECG is corrected. For this purpose, the baseline signal was modeled as a constant and a line in the lms algorithm. The two approaches were applied to the signals received from MIH-BIH database records and the results were reported and compared. When the experimental results are investigated it is observed that the line model adapts faster and tracks baseline wander.
In this paper we introduce an Modified Clipped lms (MClms) algorithm with a variable step size. In the MClms algorithm two parameters, the step size and the threshold control the convergence rate of the adaptive filte...
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ISBN:
(纸本)9781467365062
In this paper we introduce an Modified Clipped lms (MClms) algorithm with a variable step size. In the MClms algorithm two parameters, the step size and the threshold control the convergence rate of the adaptive filter coefficients and also determine the final mean-square error. The computational complexity decreased dramatically by a large threshold. However, this selection results in a low convergence rate. Since the convergence time is inversely proportional to the step size, a large step size is often selected for fast convergence. But a large step size results in an increased final mean square error. Therefore in this paper we choose a large threshold and propose a variable step size for the MClms algorithm. The advantages of this proposed variable step size and a large threshold selection are that the computation complexity is low, final mean square error is low and that the convergence is fast.
This paper presents the Variable Step Size Least Mean Square algorithm formulated in frequency domain by taking the (Fast Fourier Transform) FFT of signal obtained from filter. This way the algorithms performed better...
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ISBN:
(纸本)9781479918195
This paper presents the Variable Step Size Least Mean Square algorithm formulated in frequency domain by taking the (Fast Fourier Transform) FFT of signal obtained from filter. This way the algorithms performed better than its implementation in time domain in terms of Signal to Noise Ratio (SNR). The algorithms implemented in MATLAB with different colored noise surroundings. To evaluate the performance of the algorithm its comparison has been done with time domain. The algorithm has given 5-44% increased SNR compared to that implemented in time domain with different type of colored noises. The algorithm has also been tested in frequency domain for different step sizes.
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